Smart textiles for multimodal wearable sensing using highly stretchable multiplexed optical fiber system - Nature
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OPEN Smart textiles for multimodal
wearable sensing using highly
stretchable multiplexed optical
fiber system
Arnaldo Leal‑Junior1*, Leticia Avellar1, Anselmo Frizera1 & Carlos Marques2
This paper presents the development and application of a multiparameter, quasi-distributed smart
textile based on embedded highly stretchable polymer optical fiber (POF) sensors. The POF is
fabricated using the light polymerization spinning process, resulting a highly stretchable optical
fiber, so-called LPS-POF, with Young’s modulus and elastic limits of 15 MPa and 17%, respectively.
The differential scanning calorimetry shows a thermal stability of the LPS-POF in temperature range
of 13–40 °C. The developed sensors are based on the optical power variation, which results in a fully
portable and low-cost technique. In order to obtain a multiplexed sensor system, a technique based on
flexible light emitting diodes (LEDs) on–off keying modulation is applied, where each LED represents
the response of one sensor. The smart textile comprises of LPS-POF and three flexible LEDs embedded
in neoprene textile fabric. The performance of the system is evaluated for temperature, transverse
force and angular displacement detection at different planes. The sensors presented high linearity
(mean determination coefficient of 0.99) and high repeatability (inter-measurement deviations below
5%). The sensor is also applied in activity detection, where the principal component analysis (PCA)
was applied in the sensors responses and, in conjunction with clustering techniques such as k-means,
indicate the possibility of detecting basic activities such as walking, sitting on a chair and squatting.
The internet of things (IoT) concept mainly relies on the wireless connectivity of devices, which place demands
towards a constant evolution in wireless systems and their miniaturization. Such evolution resulted in many
developments in i ndustry1, smart cities2 and remote healthcare3 applications. The latter plays an important role
nowadays due to the demographic growth in conjunction with the population a geing4, where there is an increas-
ing demand on smart systems for remote h ealthcare5. In this scenario, it is desirable the continuous monitoring
of human activities for remote assistance, which include diagnosis, transportation in case of emergencies and
monitoring of patient’s health c ondition6. The remote health monitoring leads to advantageous features such as
reduction in the treatment cost and hospital (and clinical facilities) occupation5. As another feature of the home
monitoring, “staying-at-home” factor brings important emotional and psychological advantages for the patient
due to the possibility of performing their daily activities and the sense of independent growth in the c ommunity7.
As a popular technology for the remote health monitoring, different wearable sensors have been proposed for
the assessment of multiple parameters of the u ser8. The parameters for wearable sensors in human monitoring
include the movement assessment and analysis, body temperature, interaction forces/pressures (between human
and objects), humidity and physiological parameters, including heartbeat and breath r ates9. It is also worth to
mention the assessment of additional parameters in some cases such as arterial pulse, electromyography signals
and pulse oximetry8.
As a growing approach for wearable sensors systems, smart textiles offer the advantages of higher transpar-
ency between the sensor and the user, i.e., the sensor system is lightweight, compact and does not inhibit the
user’s movements10. The application of compact and embedded sensors in smart textiles and their advantages
of easy installation and removal have a positive effect in the system’s usability5. In light of these advantages, the
developments on the flexible electronics have enable the development of flexible wearable sensors such as the
1
Graduate Program in Electrical Engineering, Federal University of Espírito Santo (UFES), Fernando Ferrari
Avenue, Vitória 29075‑910, Brazil. 2I3N and Physics Department, Universidade de Aveiro, 3810‑193 Aveiro,
Portugal. *email: leal‑junior.arnaldo@ieee.org
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ones summarized in previously reported r eviews9. The smart textile technologies continue to point towards an
even higher miniaturization, low energy consumption and wireless connection, which are well-aligned with the
requirements of the IoT devices11. Such advances include resistive sensors embedded in fabrics patches12 and
dual core microfibers for capacitive m easurements13, including different embedment methods and c ircuitry14.
Optical fiber sensors have experienced a large growth in many fields of applications, including i ndustrial15,
structural health monitoring16 and healthcare17. In such applications, the optical fiber sensors offer advantages
such as compactness, lightweight, potential for multiplexing capabilities, intrinsically safe operation and elec-
tromagnetic interference i mmunity18. Such advantages are especially important for wearable applications, where
it is possible to obtain compact sensors with safe operation (since there is no electrical currents in the sensor’s
head) and immune to electromagnetic interferences. Such interferences occur due to assistive devices (especially
for users with health impairments) of the user as well as devices that emits electromagnetic waves, commonly
used nowadays with the widespread of portable technologies.
These advantages motivate the development of photonic-integrated textiles, which, in their first reports,
begun as clothing accessory or signaling devices19. However, with the widespread of optical fiber sensors, the
so-called photonics textiles are applied on the body temperature s ensing20, breath and heart r ates21. Many of
the reported sensors are based on fiber Bragg gratings or other wavelength-encoded sensing approaches, which
have a high precision and immunity to light source power deviations, but need an optical spectrum analyzer or
an optical interrogator, where such devices are generally bulk and non-portable with high cost (when compared
with other sensing techniques)17. In addition, such sensors employ silica optical fibers, which, despite their lower
optical loss, have a brittle nature with low impact resistance and strain limits18. It is also noteworthy that in case
of breakage, the glass fiber may puncture the user22. In order to overcome these drawbacks, advances in the
polymer processing, preparation and fabrication have enable the development of polymer optical fibers (POFs),
which present higher strain limits, flexibility and impact toughness. Their rugged surface also make POFs easier
to incorporate in textiles, where such features have been demonstrated in many works for wearable sensors for
human health a ssessment17. Furthermore, the smart textiles with POF sensors are mainly based on the intensity
variation sensing principle, in which portable sensors with lower cost (when compared to wavelength-based
sensors) are achieved.
As a drawback of the previously proposed systems, the intensity variation principle is sensitive to light source
power deviations, leading to the necessity of techniques for compensation of light source power deviations23.
Moreover, these sensors are not multiplexed, as they are mainly based on point detection of a single parameter,
if more than one point or parameter needs to be simultaneously detected, there is the need of increasing the
photodetectors, which leads to proportional reduction of the system’s portability and increase of wearable system
cost. Although POFs have Young’s modulus one order of magnitude lower than silica fibers (leading to higher
flexibility), the Young’s moduli of commercially available POFs are in the range of 1–4 GPa18, which are some
order of magnitude higher than the conventionally applied materials in fabrics/textiles (in the range of tens of
MPa)24. Thus, the integration of commercial POFs (with diameters of 0.5–1.0 mm) in a textile may lead to a
lower flexibility and freedom of movement of the clothing.
This paper presents a novel POF-integrated optoelectronic smart textile, which can overcome the limitations
discussed above. The POF used in this work is fabricated through the light polymerization spinning (LPS) pro-
cess, in which a mixture of monomers are polymerized with UV light, resulting in a higher degree of customiza-
tion for the so-called LPS-POF25. The proposed LPS-POF has Young’s modulus more than 100 times lower than
commercial POFs, which is even lower than the elastic modulus of cotton and other textile/fabric materials.
In addition, a multiplexing technique for intensity variation sensors based on side-coupling between the light
source and the LPS-POF was applied to overcome the other limitation of the current proposed smart textile, i.e.,
the multipoint and multiparameter s ensing26. The proposed LPS-POF integrated multiparameter smart textile
also takes advantage on the developments on flexible electronics, where light emitting diodes (LEDs) in flexible
substrates are used as light source in order to further enhance the system’s flexibility, usability and transparence.
The multiplexed system enables the measurements on multiple parameters in different parts of the user’s body,
whereas the high flexibility of the materials enable not only higher flexibility of the sensors, but also does not
inhibit the user’s natural movement. Thus, the proposed system was tested in the assessment of movement at
different planes, temperature, interaction force between the user and the environment and activities monitoring.
Results
Multi parameter LPS‑POF smart textile development. The POF is fabricated by the LPS process in
which there is a combination of monomers and additives instead of the extrusion of a polymer preform, con-
ventionally used in the optical fiber fabrication. This process has intrinsic advantages when compared with the
preform extrusion method such as higher customization and repeatability.
The LPS-POF has a diameter of 580 µm ± 30 µm with a core refractive index of 1.54, whereas the fiber cladding
(with 20 µm thickness) has a refractive index of 1.45. It is worth noting that such a high diameter and refractive
index difference leads to a large multimode behavior of the fiber. Although the large number of modes harms
the application of many spectral-based sensors such as fiber Bragg gratings (FBGs), it does not inhibit the use as
intensity variation-based sensors. In addition, as an acrylate-based fiber, the LPS-POF has lower optical losses
at the visible wavelength, especially at 650 nm, where the optical loss is 4 dB/m. Thus, flexible LEDs at the visible
wavelength range were used.
Regarding thermal and mechanical properties of the proposed fiber, the operation limits and the optical fiber
behavior at different conditions were tested through the differential calorimetry scanning (DSC), tensile tests
and dynamic mechanical analysis (DMA). The DSC test was performed at a temperature range of 13–200 °C
using the DSC Q200 (TA Instruments, USA), where the heat flow variation of the LPS-POF is analyzed on the
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Figure 1. Characterization of the LPS-POF: (a) DSC results, (b) Stress–strain curves in static tensile tests
and (c) Storage modulus and loss factor in DMA for different frequencies. Figure insets show a schematic
representation of the thermal and mechanical loadings in the fiber.
predefined temperature range as depicted in Fig. 1a. For the mechanical characterization, a universal tensile test
machine (Biopdi, Brazil) was employed. In the stress–strain curve shown in Fig. 1b, material parameters such as
Young’s modulus and elongation at break are estimated. However, the polymer is a viscoelastic material, which
presents time-dependent p roperties27. For this reason, the DMA was employed in the LPS-POF to analyze the
dynamic modulus, represented by the storage modulus and loss factor (as discussed in previous works28), at
oscillatory movements with different frequencies. In DMA tests, an oscillatory load with controlled displace-
ment and frequency is applied on the fiber sample, the variation of the dynamic properties, namely the storage
modulus and loss factor, as a function of the frequency in the range of 0.01–100 Hz. This range was chosen based
on the proposed application, i.e. human physiological parameters and movement/activities assessment, in which
the frequency of movement is below 100 Hz29. Figure 1c shows the results obtained in the DMA tests for the
frequency analysis. The insets in Fig. 1 show a schematic representation of the thermal and mechanical loadings
that the fiber is subjected in each characterization.
Regarding the DSC results, there is a large endothermic baseline change, related to differences between the
heat capacity of the sample and the reference in the DSC experiments (see Methods section). It is also worth
noting a change in the curve slope at about 40 °C, which is related to the material’s glass transition t emperature30.
Additionally, the melting point of the fiber is represented by an endothermic peak in the heat flow. However, such
peak was not observed in the temperature range employed, which indicates that the LPS-POF melting point is
higher than 200 °C. Thus, the thermal degradation, which generally occurs after the polymer melting, also does
not appear in this temperature range. Nevertheless, there is a small exothermic peak at about 80 °C that can be
related to the LPS-POF crystallization. Therefore, the thermal characterization of the LPS-POF indicate its suit-
ability on small temperatures as the ones for on-body application (circa 36 °C) or room temperature assessment.
For the mechanical characterization of the LPS-POF, the stress–strain curve in Fig. 1b shows a large linear range
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Figure 2. A picture and schematic representation of the proposed multifunctional smart textile. Figure also
shows the acquisition matrix with the responses of the sensors.
for the fiber of about 17% with a Young’s modulus of 15.0 MPa. In addition, the dynamic analysis presented in
Fig. 1c indicates the feasibility of the proposed LPS-POF on dynamic conditions in frequencies up to 21 Hz,
such as in human movement. In the range of 0.01–21 Hz there is no significant variation in the material’s storage
modulus (elastic component) and loss fact (ratio between the elastic and viscous component). However, in higher
frequencies, there is a sharp increase of the LPS-POF storage modulus, reaching its maximum value of 450 MPa
at 100 Hz. As the sensitivity of optical fiber sensors for mechanical parameters assessment is proportional to the
fiber Young’s modulus, such increase in the fiber Young’s modulus leads to lower sensitivity for stress-related
parameters sensing at oscillatory frequencies of 100 Hz. In addition, the variation of the sensitivity also results
in a nonlinear behavior of the sensor.
The LPS-POF has promising mechanical features and properties for mechanical sensing with the possibility
of embedding on textiles without changing the textile stiffness, which makes it suitable for multiparameter sens-
ing in smart textiles. In order to achieve such multiparameter sensing using low cost intensity variation-based
sensors, a multiplexing technique was employed in which there is a side coupling of the light source in the fiber
and the end faces of the fiber are connected to photodetectors. In this case, the light source is a LED in flexible
substrate to ensure a high flexibility to the system embedded in the textile fabric. Then, an on–off keying (OOK)
modulation is applied to each flexible LED in a way that only one LED is activated at time. Thus, there is no
simultaneous activation of each LED, where the position of each LED represents a sensor point in the fiber. For
this reason, a quasi-distributed sensor system is achieved using only one fiber and photodetector, where the
number of sensors in the system is equal to the number of LEDs side-coupled to the fiber. A microcontroller
FRDM-KL2Z (NXP, Netherlands) is used for the activation signals to the LED and for the acquisition the data
from the photodetector IF-D92 (Industrial Fiber Optics, USA) through its 16-bit analog-to-digital converter. As
another key process in the microcontroller, there is a synchronization between the LED activation and the data
acquisition from the photodetector, where the data acquired is positioned in a matrix (as shown in Fig. 2). Each
column represents the active LED and the lines are the data acquired through time. In this proof of concept, three
flexible LEDs were used (as also shown in Fig. 2) and the matrix has three columns (for LEDs 1, 2 and 3). The data
at each column is the photodetector signal when each LED is active, i.e., the data in column 1 is the one gathered
when LED 1 is active, in column 2 when LED 2 is activated and so on. The proposed multiplexing technique not
only allows the position assessment of mechanical disturbances in the LPS-POF, but also the multiparameter
sensing by characterizing the sensors with respect to each of the desired parameter26. In addition, the system
has scalability and a higher number of sensors can be used with few centimeters distance between them31,32.
Thereafter, the sensor system (comprised of the LPS-POF and the flexible LEDs) is sewed between two lay-
ers of a neoprene textile fabric. Figure 2 presents a picture of the proposed smart textile, where the optical fiber
connector and the cables for the LEDs supply are also shown. Both of these cables connect in the microcontroller
and its shield with circuit the board for the photodetectors and LEDs activation. Thus, the proposed system is
fully portable solution in which the acquisition module comprised of the two photodetectors and the micro-
controller can storage the data in a SD card or send it wirelessly using a Bluetooth connection to a host device
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Figure 3. Temperature analysis of the LPS-POF embedded textile. (a) Temperature characterizations. (b)
Temperature responses of each sensor for different heat spots.
(such as computer, tablet or smartphone) with acquisition frequency of 100 Hz. As an important parameter
for wearable applications, the system can be considered a lightweight solution due to its total weight of 400 g,
including microcontroller, photodetectors, LEDs and batteries. In addition, the proposed smart textile has low
power consumption with an average consumption of 150 mA was obtained for the whole system (i.e., photo-
detectors, microcontroller and LEDs), which enable an autonomy as high as 10 h using commercially available
10,000 mAh power banks.
The proposed system is characterized and validated with respect to different movement and physiological
parameters. First, different temperature profiles are applied on the sensors and their temperature responses are
evaluated for the possibility of on-body temperature sensing. Then, controlled pressures are applied at each sensor
in order to detect the interaction pressure between the user and the environment that can be used for activity
monitoring. In addition, different movements of bending and torsion at different planes are applied on the textile
in order to verify the system’s capacity for movement analysis applications. Finally, the smart textile is positioned
on the lower back of a volunteer and the sensors responses are analyzed for commonly activities on daily routine,
including walking, squatting and sitting, where the signals are analyzed using the Principal Component Analysis
(PCA) in conjunction with clustering technique (such as k-means), which leads to the activities detection.
Smart textile responses to thermal and mechanical parameters. The LPS-POF embedded smart
textile was tested in different conditions in order to verify its suitability on measuring multiple parameters,
where the multiplexing technique based on the LEDs modulation enables the simultaneous measurement of
multiple parameters in multiple points on the LPS-POF. In this case, the tests were performed in the textile with
three measurement points. Figure 3 presents the temperature response of each sensor, where the tests were per-
formed in a range of 20–40 °C due to both the intended application (room temperature monitoring and on-body
applications) and the thermal restrictions in the fiber presented in Fig. 1a. The temperature characterization of
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Figure 4. Force analysis of the LPS-POF embedded textile. (a) Transmitted optical power attenuation as
function of time for forces applied at different sensors. (b) Force characterizations. (c) Force map from the
sensors responses with a force applied on the textile.
each sensor (Fig. 3a) shows a linear behavior of all sensors in the mean and standard deviations of five tests,
where the sensors 1 presented the highest temperature sensitivity. The temperature increase in the LPS-POF
leads to refractive index variation due to the thermo-optic effect, which results in variations in the transmitted
optical power, the differences in the sensitivities can be related to anisotropy in the fiber material33. From the lin-
ear regressions obtained in the sensors characterizations in Fig. 3a, it is possible to estimate a temperature distri-
bution in the textile, as shown in Fig. 3b. In this case, a thermal blower is positioned in different locations along
the fiber, leading to both distributed and concentrated temperature increase in different regions. The capability
of sensing the temperature distribution in the textile was demonstrated, where the sensors also presented low
cross-sensitivity between them, demonstrating the feasibility of the proposed multiplexing technique. Regard-
ing the temperature responses, the solid lines are the responses of the sensors applying the linear regressions,
whereas the shaded lines are the temperature uncertainty of the sensors, considering the standard deviations on
the characterizations shown in Fig. 3a. The positions and temperatures of the heating spots on the optical fiber
are also presented in Fig. 3b, where it can be seen a temperature increase starting in the Sensor 3 and ending in
Sensor 1. Thus, the proposed sensor system can be used for body temperature monitoring and can be applied in
the interface between the user and an assistive device for microclimate assessment.
Thereafter, different forces are applied at each sensor to obtain their responses at transverse force conditions,
which can be correlated to the applied pressure by considering the area of each sensor. In order to show the low
crosstalk between sensors, Fig. 4a shows the sensors responses as a function of time for a force of 100 N applied
at one sensor at a time (starting from Sensor 1). It is possible to observe the low cross-sensitivity between sensors,
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Figure 5. Sensors responses with angular displacement on different planes.
i.e., when the force is applied directly on one sensor, there is no significant signal variation in the others, showing
the feasibility of the proposed LED modulation multiplexing technique for intensity-based sensors. In Fig. 4b,
the force characterization is presented, where high linearity of all sensors and low standard deviation between
tests is shown. By applying the linear regression in the sensors responses, it is possible to obtain a pressure/force
map of the interaction between the sensor and the user (or environment), as shown in Fig. 4c. In this case, a
rectangular object (resembling a chair support) is positioned on the textile and the force map is presented in
Fig. 4c, which indicates the possibility of using the proposed smart textile with a mesh of sensors to evaluate the
interaction pressure between the user and the environment, such as chairs and beds. Such evaluation is important
to provide a remote monitoring of the user’s activities and prevent pressure ulcers.
Comparing the force and temperature responses, the sensors presented higher optical signal variation in the
force tests when compared with the temperature tests, see Figs. 3a and 4b. The signal variations are more than
3 times higher in the force tests than in the temperature experiments, which results in a higher signal-to-noise
ratio of the force response (compared with the temperature response). Thus, if the sensors responses as function
of time are compared, as shown in Figs. 3b and 4a for temperature and force responses, respectively, there is a
higher stability of the optical power variation in the force response.
The smart textile sensors responses under displacements applied at different planes are presented in Fig. 5.
Bending at different planes and torsions were applied with controlled angles. For the bending, the angles were
0° to 90° and 0° to − 90°, whereas, in the torsion assessment, angular displacements ranging from 0° to 180° were
applied, as depicted in Fig. 5 inset. Comparing the responses of each sensor, which are obtained at the angular
displacements in different planes, it is possible to observe differences in the sensors behavior, which can be used
for the classification of each movement in multiple planes. It is also noteworthy that Sensors 1 and 2 presented
the highest bending sensitivity, related to the region where the bending was applied (see Fig. 5 inset), resulting
in a higher stress in Sensors 1 and 2. In the torsion case, the highest optical power variation was also obtained
in Sensor 1, whereas, once again, the Sensor 3 presented the lowest signal variation. However, the differences
in the sensors sensitivities obtained in each case can be used for the estimation of the multiplane displacements
applied on the textile, using techniques such as transfer matrix for 3D plane displacements a ssessment34, where
a system of equations obtained in the sensors characterizations are used for the angle assessment of each plane.
It enables the remote human movement analysis using wearable sensors that do not inhibit the natural pattern
of the user’s movements.
As shown in Fig. 3, the sensors are also sensitive to temperature variations. Thus, temperature variations
interfere on the bending and force assessment. Although the results presented in Figs. 4 and 5 were obtained in
constant temperature conditions, temperature variations can occur in practical applications. In order to miti-
gate the temperature influence on the sensors’ responses, two approaches are considered. The first approach is
based on the difference between sensors responses for temperature and strain-related parameters (as previously
validated in temperature-compensated s ystems35), considering their previously characterized sensitivities with
respect to temperature, angle and force, in this case. Furthermore, the use of the smart textile in dynamic move-
ment applications leads to an additional possibility of temperature compensation. In practical applications of the
proposed textile, the temperature variation rate is lower than the one of the strain-related parameters (such as
force and angle). This behavior leads to differences in the frequency components of the temperature and strain,
where the lower frequencies are related to the temperature. Therefore, the temperature influence on the strain
response is mitigated by filtering the low frequencies components on the sensor responses, as demonstrated in
previous works36.
It is also worth noting that the smart textile characterization with respect to temperature and force show a
high repeatability of the sensors with low standard deviation (0.004 a.u.) in the temperature assessment after 3
sequential tests (see the error bars in Fig. 3a). Moreover, the error bars are not visible in Fig. 4b due to its low
standard deviation (0.005 a.u.). As another indicator of the sensors’ consistency, the bending tests, whose results
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Figure 6. LPS-POF embedded smart textile for user’s activity monitoring. (a) Time-domain analysis. (b) Scatter
plot for the activities monitoring.
are shown in Fig. 5, show the sensor reversibility, since the sensors responses returned to their initial condition
without significant residual strains after the bending is performed. The maximum reversibility error (obtained
by the comparison between the sensors responses before and after the bending) is 0.03 a.u., considering all three
sensors.
In the last evaluation of the smart textile performance, the capability of detecting user’s activities is assessed
by means of positioning the textile in a healthy volunteer. The textile is positioned on the user’s lower back. Its
positioning in the lower back enables to acquire the signals from the activities characterized by strong correla-
tion with user’s trunk displacement, as lower limb movements generally result in variations on the trunk, the
smart textile can be used on the detection of gait-related activities as well. Thus, the user was asked to perform
three common movements in daily routing: walking in self-selected speed, squatting and seat on a chair. The
sensors responses for each case are presented in Fig. 6a, where the sensors are analyzed in the time domain. The
time domain response shows significant differences in the sensors responses, especially in the squatting activity,
where Sensor 2 shows the highest signal attenuation, which is related to its positioning at the center of user’s
lower back, leading to higher displacements in the fiber when compared with Sensors 1 and 3. Another offset in
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the sensors’ responses were found when the user sits on a chair, where the back support of the chair resulted in
an attenuation on the transmitted optical power. The walking activity is the one that induced the lowest optical
power variation in the sensors. In order to enhance the activity detection performance, the PCA was applied in
the sensors responses. The PCA represents the data in a new coordinate system by using linear transformations
and is a widely used technique for dimensionality reduction and as preprocessing for clustering techniques37. In
this case, the PCA was applied in the data to show the possibility of activity detection and identification using
the proposed smart textile when used in conjunction with a clustering technique such as k-means38. Figure 6b
shows the scatter plot of first two principal components for the sensors responses obtained on each activity, these
two principal components represent 98% of all variability, mainly related to time-domain responses of Sensors 2
and 3, which indicate the possibility of dimensionality reduction by using just the first two principal components
(instead of all components). Furthermore, the PCA results in three separate groups, which are identified as three
clusters, representing each performed activity. It is important to mention that a higher number of observations
was obtained in the walking and sitting activities due to their longer duration when compared with squatting
activity, as shown Fig. 6a. Thus, the PCA in conjunction with k-means (or other clustering technique) result
in a feasible option for the activities detection using the proposed smart textile. It is also worth noting that the
proposed textile has scalability, where a higher number of sensors can be used, which can lead to the detection
of a higher number of activities, depending on the sensor system positioning on the user.
The experimental results obtained with the proposed smart textile shows the feasibility of this approach in a
novel, multiparameter and transparent sensor system that can be used in multiple applications. It is possible to
envisage that the next generation of the textile fabrics will be embedded with sensor system for the assessment of
the user’s health condition and activities, where the proposed technology (i.e. LPS-POF embedded in conjunction
with multiplexing techniques based on LEDs modulation) can be a key technology for transparent multipurpose
sensor systems. The proposed smart textile can be also integrated with IoT modules for remote health monitor-
ing. In addition, due to the scalability of the proposed technique, it is also possible to envisage the development
of a complete clothing embedded with the proposed sensors for the assessment of multiple parameters, such as
heart and breath rates, kinematic parameters of the human, interaction forces, body temperature, just to name
a few. All these sensors transmitting to a home gateway and IoT modules for the remote health monitoring,
which can also be integrated with novel classifiers and neural network in order to extract all the physical and
physiological information of the user.
Discussions
In this paper, a novel smart textile based on multiplexed intensity variation-based sensors was proposed. The
multiplexing technique is based on an OOK modulation in the LEDs, which results in a quasi-distributed sensor
system with low cross-sensitivity. In contrast with other popular quasi-distributed optical fiber sensors, such as
FBGs, the proposed approach does not need neither specialized equipment for the sensor fabrication nor high
cost (and generally bulk) interrogators for the signal acquisition. Therefore, the proposed technique is fully
portable and low cost, which enable a plethora of applications in remote health monitoring by either transparent
wearable sensors or smart homes. In addition, the LPS-POF used in the smart textile has remarkable flexibility
(Young’s modulus of 15 MPa) in conjunction with high strain limits (about 17%), which can be embedded in
clothing without restricting the user’s movement and results in sensors with high sensitivity and can be used in
a large range of dynamic movements, as verified in the DMA experiments.
The LPS-POF and flexible LEDs were embedded in between two layers of neoprene textile fabric for the
multiplexed sensor system. The temperature and interaction forces characterization showed the high sensitiv-
ity and linearity of the sensor system, which can also measure the temperature profile and force map with high
repeatability and resolution. Then, the movement analysis using the proposed textile was performed by means of
multiplane angle assessment using the textile sensors for bending at different planes as well as torsion, where the
sensors presented different sensitivities with the possibility of simultaneous assessment of angular displacement at
different planes. Finally, the smart textile was positioned on the user’s lower back and was able of identifying basic
activities in daily life such as walking, siting and squatting by the analysis of the sensors responses using PCA in
conjunction with clustering techniques. Therefore, the proposed device is a feasible option for novel transparent
wearable sensors for remote health monitoring, which can be scalable for a fully instrumented clothing with
smart sensors. Future works include the development and validation of the smart clothing for simultaneous and
remote evaluation of multiple parameters.
Methods
LPS‑POF fabrication. The LPS-POF fabrication is performed in three steps: (1) addition of a liquid mix-
ture of monomers and additive in a dosing system, where the main monomer is the Bisphenol-A acrylate due
to its combination of high elasticity and optical transparency. (2) The liquid mixture passes through a spinneret
in order to obtain the cylindrical shape. (3) The UV-curing is performed for the polymerization followed by an
axial deformation stage to obtain the desired diameter for the optical fiber.
DSC tests. In order to characterize the fiber thermal transitions, DSC is performed, in which the LPS-POF
enthalpy variation is compared with a thermally inert reference39. The DSC equipment employed is the DSC
Q200 (TA Instruments, USA), where the heat flow variation in the sample is analyzed with respect to the tem-
perature. In this test, the offset in the heat flow variation curve is related to the polymer glass transition tempera-
ture, whereas an endothermic peak corresponds to its melting temperature.
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Tensile and DMA tests. The tensile tests were performed using a universal testing machine (Biopdi, Bra-
zil) in which the fiber was positioned in the machine’s clamps for the axial strain tests, where the displacement
(strain) and force (stress) were continuously monitored. The test occurs until the fiber breakage, where it is pos-
sible to infer the strain limits of the LPS-POF. In addition, the Young’s modulus of the fiber is calculated as the
ratio between the stress and strain in the elastic region (linear region) of the stress–strain curve. All the tensile
tests were made with constant strain rate of 1 mm/min. Then, the DMA tests are performed by means of applying
an oscillatory load in the sample with controlled displacement and frequency at constant temperature condi-
tion. The DMA 8000 (Perkin Helmer, USA) was used in the experiments in the axial strain configuration with a
maximum displacement of 1 mm, where each measurement was performed 3 times in an isothermal period of
10 min. The temperature in the tests was 25 °C.
Temperature characterization setup. In the temperature characterization tests, Sensors 1, 2 and 3 were
positioned in a thermoelectric Peltier plate TEC-12706 (Heibei IT, China) with closed loop temperature control
TED 200C (Thorlabs, USA). The temperature range was 20–40 °C in 5 °C steps, where the isothermal period was
5 min in order to ensure a constant temperature at each sensor. For the temperature profile tests, a temperature-
controlled hot air blower 858D (QWERTOUY, USA) was employed and positioned at different regions of the
smart textile.
Force characterization setup. For the force characterization, calibrated weights with a known mass were
positioned on the top of each sensor for about 10 s. All the sensors were tested in a range of 0–150 N. However,
Sensors 1 and 3 have different forces due to the dimensions of the weights, which can apply a force on Sensor
2 due to the proximity of such sensors. In addition, the force map characterization was performed by position
a weight with larger dimensions in the center of the smart textile, leading to a force distribution in the sensors.
Angular displacement characterization setup. The angular displacement tests were performed by
manually bending the textile with the aid of a goniometer at the different planes, where the goniometer was
previously aligned with the bending plane and the angular displacements in the range of 0° to 90° for bending
and 0° to 180° for torsion were performed.
Human activity monitoring protocol. In the smart textile validation test, a healthy volunteer (male,
29 years) positioned the smart textile in his lower back with the aid of elastic bands. The test comprises of walk-
ing in a 5-m room at constant velocity and, at the end of the 5 m walk, the user performs a squat. Then, there
is 180° turn and the user returns to the end of the room, where a chair is positioned. The user sits on the chair
and the test ends at about 30 s later. We have the informed consent of all participants and the tests were made
in accordance with the guidelines of the national health council with the protocols approved by Research Eth-
ics Committee through the National Commission in Research Ethics—CONEP—(Certificate of Presentation
for Ethical Appreciation—CAAE: 64797816.7.0000.5542). The responses of all three sensors were acquired for
each activity, i.e. walking, squatting and sitting. The signal processing was performed using PCA, since it is a
widely used statistical tool for multivariable data analysis (details on the PCA algorithm can be found in Jollife
and Cadima37), where the data distributed in the principal components can be classified using techniques such
as the k-means clustering.
Received: 28 April 2020; Accepted: 27 July 2020
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Acknowledgements
Authors would like to thank LabPetro under the partnership agreement 0050.0022844.06.4 for the use of the
DMA and DSC equipment. Authors also would like to thank Garry Berkovic, Ehud Shaffir and Oleg Palchik
for providing the LPS-POF. This research is financed by FAPES (85426300, 84336650 and 2020-F057G), CNPq
(304049/2019-0) and Petrobras (2017/00702-6). C. Marques acknowledges Fundação para a Ciência e a Tecno-
logia (FCT) through the CEECIND/00034/2018 (iFish project) and this work was developed within the scope
of the project i3N, UIDB/50025/2020 & UIDP/50025/2020, financed by national funds through the FCT/MEC.
This work is also funded by national funds (OE), through FCT, I.P., in the scope of the framework contract
foreseen in the numbers 4, 5 and 6 of the article 23, of the Decree-Law 57/2016, of August 29, changed by Law
57/2017, of July 19.
Author contributions
A.L.-J. and L. A. characterized the sensors, conceived and fabricated the smart textile. C.M., A.L.-J. and A.F.
analyzed the data. All authors wrote the paper and revised the paper.
Competing interests
The authors declare no competing interests.
Additional information
Correspondence and requests for materials should be addressed to A.L.-J.
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